Optimization using evolutionary metaheuristic techniques: a brief review

• Autor(a) principal: Radhika, Sajja
• Data de Publicação: 2018
• Outros Autores: Chaparala, Aparna
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Operations & Production Management (Online)
• Texto Completo: https://bjopm.org.br/bjopm/article/view/425
• topic: Optimization; Evolutionary algorithms; Meta-heuristic techniques; Applications.

Optimization is necessary for finding appropriate solutions to a range of real life problems. Evolutionary-approach-based meta-heuristics have gained prominence in recent years for solving Multi Objective Optimization Problems (MOOP). Multi Objective Evolutionary Approaches (MOEA) has substantial success across a variety of real-world engineering applications. The present paper attempts to provide a general overview of a few selected algorithms, including genetic algorithms, ant colony optimization, particle swarm optimization, and simulated annealing techniques. Additionally, the review is extended to present differential evolution and teaching-learning-based optimization. Few applications of the said algorithms are also presented. This review intends to serve as a reference for further work in this domain.